Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests.

Journal: Journal of rehabilitation research and development
Published Date: Jan 1, 2016

Abstract

Cervical spondylotic myelopathy (CSM) is a chronic spinal disorder in the neck region. Its prevalence is growing rapidly in developed nations, creating a need for an objective assessment tool. This article introduces a system for quantifying hand motor function using a handgrip device and target tracking test. In those with CSM, hand motor impairment often interferes with essential daily activities. The analytic method applied machine learning techniques to investigate the efficacy of the system in (1) detecting the presence of impairments in hand motor function, (2) estimating the perceived motor deficits of CSM patients using the Oswestry Disability Index (ODI), and (3) detecting changes in physical condition after surgery, all of which were performed while ensuring test-retest reliability. The results based on a pilot data set collected from 30 patients with CSM and 30 nondisabled control subjects produced a c-statistic of 0.89 for the detection of impairments, Pearson r of 0.76 with p < 0.001 for the estimation of ODI, and a c-statistic of 0.82 for responsiveness. These results validate the use of the presented system as a means to provide objective and accurate assessment of the level of impairment and surgical outcomes.

Authors

  • Sunghoon I Lee
    Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CA.
  • Alex Huang
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Bobak Mortazavi
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Charles Li
    Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CA.
  • Haydn A Hoffman
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Jordan Garst
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Derek S Lu
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Ruth Getachew
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Marie Espinal
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Mehrdad Razaghy
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Nima Ghalehsari
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Brian H Paak
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Amir A Ghavam
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Marwa Afridi
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Arsha Ostowari
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Hassan Ghasemzadeh
    School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164 USA.
  • Daniel C Lu
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Majid Sarrafzadeh
    Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CA.

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